Quantum computing research: An in-depth exploration

Islam, Ray

Kanishk Dwivedi, Majid Haghparast, Tommi Mikkonen

Abstract Quantum software engineering is advancing in the domain of quantum computing research and application, yet the documentation is scattered. The slow transition from Von-Neumann based computation systems to quantum systems, and conserving the fundamental computing principles in software development and software engineering helps in enrichment of quantum software development. The evolution of quantum computing over the past years shows a shift in the domain of classical computation to quantum computation in the years to come. Future applications such as, quantum AI and quantum machine learning will benefit from quantum software engineering. This survey collects and explores the various documentations in the domain of quantum systems and quantum software engineering. The survey provides an in-depth exploration of quantum programming languages, which is combined with explanations of quantum computing’s fundamentals. The review also goes in-depth about quantum software engineering and quantum software life cycle development, outlining the quantum software reuse methodology that is introduced in the quantum software lifecycle development domain.

Saima Rafi, Muhammad Azeem Akbar, Sajjad Mahmood

This study investigates the challenges associated with the implementation of quantum computing (QC) in the context of 6G technology. It builds upon prior research to explore the pivotal role QC plays in shaping the future landscape of telecommunications. Through an in-depth analysis, we identify and examine key application areas of QC within the realm of 6G, drawing insights from existing literature. By dissecting these challenges, our research aims to enhance understanding of the effective integration of QC into 6G networks, offering valuable insights for both industry practitioners and academic scholars. Ultimately, this study underscores the transformative potential of QC within the framework of 6G technology and lays a foundation for further exploration in this rapidly evolving field.

Михайло Миколайович Савчук, Andrii Fesenko

Samuel Yen-Chi Chen, Zhiding Liang

Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms. Concurrently, the exploration of systematic and automated approaches for designing high-performance quantum circuit architectures for QML tasks has gained prominence, as these methods empower researchers outside the quantum computing domain to effectively utilize quantum-enhanced tools. This tutorial will provide an in-depth overview of recent breakthroughs in both areas, highlighting their potential to expand the application landscape of QML across diverse fields.

Mikio Nakahara